Symptoms and side effects like fatigue; hair loss, sore mouth, pain, and problems sleeping are important issues during and following cancer treatment. Most research on symptoms is embedded in a single symptom framework but the reality of the experience of cancer treatment is that symptoms occur in groups or dusters. This gap between the paradigm used to guide research and the "real world" experience of people with cancer is a major limitation in building the knowledge base for symptom management in cancer care. The purpose of this project is to address this gap by identifying symptom clusters experienced during cancer treatment, determining if specific demographic and clinic variables predict symptom cluster membership, and examining the relationship of symptom cluster membership to mood and physical function over time. We will use data collected in four prior studies that included information about symptoms, mood, and function but did not all use the same approach for getting this information. The fact that the studies are somewhat different is a strength for this project because we will be doing the statistical analysis separately for each study and will be able to look at the clusters to see if they look similar based upon type of treatment (radiation treatment or chemotherapy) to confirm or cross-validate the findings. We will explore predictors of symptom cluster membership using variables such as age, stage of disease, and comorbidity. This information is helpful because it may eventually tell us who is at highest risk for experiencing numerous severe symptoms. Hierarchical cluster analysis will be used to identify and cross-validate the symptom clusters in each type of cancer treatment and by cancer diagnosis (breast or lung cancer). After cluster membership is established, we will use hierarchical linear modeling to examine the relationship of cluster membership to mood and function. This project is innovative because it addresses an unstudied problem and will provide information essential to other investigators who are moving to multiple symptom models. The results of this project will provide important information about an approach that is assumed to be useful in pursuing work on symptom clusters but has never been applied in published work on cancer symptoms except in developing assessment tools. Because we are doing our analyses within two types of cancer treatment and cross-validating within each type of treatment for breast cancer and across two types of treatment for lung cancer, the results of the analyses will provide information about how well the symptom clusters hold up when the two different treatments are compared and may challenge our traditional clinical assumption that the clusters of symptoms people experience are a result of type of treatment.